import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class        UpperCamelCase						(				SCREAMING_SNAKE_CASE_	,	unittest.TestCase				):
						a__		:str      =			ShapEPipeline
						a__		:Dict      =			['''prompt''']
						a__		:Any      =			['''prompt''']
						a__		:List[Any]      =			[
						    '''num_images_per_prompt''',
						    '''num_inference_steps''',
						    '''generator''',
						    '''latents''',
						    '''guidance_scale''',
						    '''frame_size''',
						    '''output_type''',
						    '''return_dict''',
						]
						a__		:Tuple      =			False





						@property
						def        A_							(self						)				->				str:
												return 32





						@property
						def        A_							(self						)				->				Dict:
												return 32





						@property
						def        A_							(self						)				->				str:
												return self.time_input_dim * 4





						@property
						def        A_							(self						)				->				Dict:
												return 8





						@property
						def        A_							(self						)				->				Any:
												UpperCamelCase_		:							Union[str, Any]  =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)
												return tokenizer





						@property
						def        A_							(self						)				->				Optional[int]:
												torch.manual_seed(0						)
												UpperCamelCase_		:							Optional[int]  =		CLIPTextConfig(
												    bos_token_id=0			,     eos_token_id=2			,     hidden_size=self.text_embedder_hidden_size			,     projection_dim=self.text_embedder_hidden_size			,     intermediate_size=37			,     layer_norm_eps=1E-0_5			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=1_000			,     )
												return CLIPTextModelWithProjection(snake_case__						)





						@property
						def        A_							(self						)				->				Optional[int]:
												torch.manual_seed(0						)

												UpperCamelCase_		:							Union[str, Any]  =		{
												    """num_attention_heads""": 2,
												    """attention_head_dim""": 16,
												    """embedding_dim""": self.time_input_dim,
												    """num_embeddings""": 32,
												    """embedding_proj_dim""": self.text_embedder_hidden_size,
												    """time_embed_dim""": self.time_embed_dim,
												    """num_layers""": 1,
												    """clip_embed_dim""": self.time_input_dim * 2,
												    """additional_embeddings""": 0,
												    """time_embed_act_fn""": """gelu""",
												    """norm_in_type""": """layer""",
												    """encoder_hid_proj_type""": None,
												    """added_emb_type""": None,
												}

												UpperCamelCase_		:							List[Any]  =		PriorTransformer(**snake_case__						)
												return model





						@property
						def        A_							(self						)				->				str:
												torch.manual_seed(0						)

												UpperCamelCase_		:							int  =		{
												    """param_shapes""": (
												        (self.renderer_dim, 93),
												        (self.renderer_dim, 8),
												        (self.renderer_dim, 8),
												        (self.renderer_dim, 8),
												    ),
												    """d_latent""": self.time_input_dim,
												    """d_hidden""": self.renderer_dim,
												    """n_output""": 12,
												    """background""": (
												        0.1,
												        0.1,
												        0.1,
												    ),
												}
												UpperCamelCase_		:							List[str]  =		ShapERenderer(**snake_case__						)
												return model





						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							List[Any]  =		self.dummy_prior
												UpperCamelCase_		:							Union[str, Any]  =		self.dummy_text_encoder
												UpperCamelCase_		:							Optional[Any]  =		self.dummy_tokenizer
												UpperCamelCase_		:							Optional[Any]  =		self.dummy_renderer

												UpperCamelCase_		:							str  =		HeunDiscreteScheduler(
												    beta_schedule="""exp"""			,     num_train_timesteps=1_024			,     prediction_type="""sample"""			,     use_karras_sigmas=snake_case__			,     clip_sample=snake_case__			,     clip_sample_range=1.0			,     )
												UpperCamelCase_		:							Any  =		{
												    """prior""": prior,
												    """text_encoder""": text_encoder,
												    """tokenizer""": tokenizer,
												    """renderer""": renderer,
												    """scheduler""": scheduler,
												}

												return components





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=0						)				->				List[Any]:
												if str(snake_case__						).startswith("""mps"""						):
																		UpperCamelCase_		:							Any  =		torch.manual_seed(snake_case__						)
												else:
																		UpperCamelCase_		:							Any  =		torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
												UpperCamelCase_		:							List[Any]  =		{
												    """prompt""": """horse""",
												    """generator""": generator,
												    """num_inference_steps""": 1,
												    """frame_size""": 32,
												    """output_type""": """np""",
												}
												return inputs





						def        A_							(self						)				->				str:
												UpperCamelCase_		:							List[str]  =		"""cpu"""

												UpperCamelCase_		:							str  =		self.get_dummy_components()

												UpperCamelCase_		:							Tuple  =		self.pipeline_class(**snake_case__						)
												UpperCamelCase_		:							Any  =		pipe.to(snake_case__						)

												pipe.set_progress_bar_config(disable=snake_case__						)

												UpperCamelCase_		:							Optional[int]  =		pipe(**self.get_dummy_inputs(snake_case__						)						)
												UpperCamelCase_		:							List[Any]  =		output.images[0]
												UpperCamelCase_		:							Optional[Any]  =		image[0, -3:, -3:, -1]

												assert image.shape == (20, 32, 32, 3)

												UpperCamelCase_		:							str  =		np.array(
												    [
												        0.00_039_216,
												        0.00_039_216,
												        0.00_039_216,
												        0.00_039_216,
												        0.00_039_216,
												        0.00_039_216,
												        0.00_039_216,
												        0.00_039_216,
												        0.00_039_216,
												    ]						)

												assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2





						def        A_							(self						)				->				List[str]:
												# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
												self._test_inference_batch_consistent(batch_sizes=[1, 2]						)





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							List[Any]  =		torch_device == """cpu"""
												UpperCamelCase_		:							List[str]  =		True

												self._test_inference_batch_single_identical(
												    batch_size=2			,     test_max_difference=snake_case__			,     relax_max_difference=snake_case__			,     )





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							Optional[Any]  =		self.get_dummy_components()
												UpperCamelCase_		:							str  =		self.pipeline_class(**snake_case__						)
												UpperCamelCase_		:							int  =		pipe.to(snake_case__						)
												pipe.set_progress_bar_config(disable=snake_case__						)

												UpperCamelCase_		:							Union[str, Any]  =		1
												UpperCamelCase_		:							List[Any]  =		2

												UpperCamelCase_		:							Tuple  =		self.get_dummy_inputs(snake_case__						)

												for key in inputs.keys():
																		if key in self.batch_params:
																								UpperCamelCase_		:							List[str]  =		batch_size * [inputs[key]]

												UpperCamelCase_		:							Any  =		pipe(**snake_case__			,     num_images_per_prompt=snake_case__						)[0]

												assert images.shape[0] == batch_size * num_images_per_prompt


@slow
@require_torch_gpu
class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self						)				->				Any:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





						def        A_							(self						)				->				int:
												UpperCamelCase_		:							Tuple  =		load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/shap_e/test_shap_e_np_out.npy"""						)
												UpperCamelCase_		:							Optional[int]  =		ShapEPipeline.from_pretrained("""openai/shap-e"""						)
												UpperCamelCase_		:							Optional[Any]  =		pipe.to(snake_case__						)
												pipe.set_progress_bar_config(disable=snake_case__						)

												UpperCamelCase_		:							Dict  =		torch.Generator(device=snake_case__						).manual_seed(0						)

												UpperCamelCase_		:							int  =		pipe(
												    """a shark"""			,     generator=snake_case__			,     guidance_scale=15.0			,     num_inference_steps=64			,     frame_size=64			,     output_type="""np"""			,     ).images[0]

												assert images.shape == (20, 64, 64, 3)

												assert_mean_pixel_difference(snake_case__			,     snake_case__						)







from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
				A						=			input('Enter image url: ').strip()
				print(F'''Downloading image from {url} ...''')
				A						=			BeautifulSoup(requests.get(url).content, 'html.parser')
				# The image URL is in the content field of the first meta tag with property og:image
				A						=			soup.find('meta', {'property': 'og:image'})['content']
				A						=			requests.get(image_url).content
				A						=			F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
				with open(file_name, 'wb') as fp:
								fp.write(image_data)
				print(F'''Done. Image saved to disk as {file_name}.''')